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![Page 1: Graphs What are Graphs? General meaning in everyday math: A plot or chart of numerical data using a coordinate system. Technical meaning in discrete.](https://reader036.fdocuments.net/reader036/viewer/2022070401/56649f1c5503460f94c3345a/html5/thumbnails/1.jpg)
Graphs
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What are Graphs?
General meaning in everyday math: A plot or chart of numerical data using a coordinate system.
Technical meaning in discrete mathematics:A particular class of discrete structures (to be defined) that is useful for representing relations and has a convenient webby-looking graphical representation.
Not
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Applications of Graphs
Potentially anything (graphs can represent relations, relations can describe the extension of any predicate).
Apps in networking, scheduling, flow optimization, circuit design, path planning.
More apps: Geneology analysis, computer game-playing, program compilation, object-oriented design, …
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Terminology
A simple graph G = (V,E) consists of V, a nonempty set of vertices, and E, as set of unordered pairs of distinct elements of V called edges.
A multigraph G = (V,E) consists of a set V of vertices, a set E of edges, and a function f from E to {{u,v} | u,v V, u ≠I v}. The edges e1 and e2 are called multiple or parallel edges if f(e1) = f(e2).
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Simple Graphs
Correspond to symmetric,irreflexive binary relations R.
A simple graph G=(V,E)consists of:– a set V of vertices or nodes (V corresponds to the
universe of the relation R),– a set E of edges / arcs / links: unordered pairs of
[distinct] elements u,v V, such that uRv.
Visual Representationof a Simple Graph
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Let V be the set of states in the far-southeastern U.S.:–I.e., V={FL, GA, AL, MS, LA, SC, TN, NC}
Let E={{u,v}|u adjoins v}={{FL,GA},{FL,AL},{FL,MS}, {FL,LA},{GA,AL},{AL,MS}, {MS,LA},{GA,SC},{GA,TN}, {SC,NC},{NC,TN},{MS,TN}, {MS,AL}}
Example of a Simple Graph
TN
ALMS
LA
SC
GA
FL
NC
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Multigraphs
Like simple graphs, but there may be more than one edge connecting two given nodes.
A multigraph G=(V, E, f ) consists of a set V of vertices, a set E of edges (as primitive objects), and a functionf:E{{u,v}|u,vV uv}.
E.g., nodes are cities, edgesare segments of major highways.
Paralleledges
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Pseudographs
Like a multigraph, but edges connecting a node to itself are allowed. (R may be reflexive.)
A pseudograph G=(V, E, f ) wheref:E{{u,v}|u,vV}. Edge eE is a loop if f(e)={u,u}={u}.
E.g., nodes are campsitesin a state park, edges arehiking trails through the woods.
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Directed Graphs
Correspond to arbitrary binary relations R, which need not be symmetric.
A directed graph (V,E) consists of a set of vertices V and a binary relation E on V.
E.g.: V = set of People,E={(x,y) | x loves y}
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Directed Multigraphs
Like directed graphs, but there may be more than one arc from a node to another.
A directed multigraph G=(V, E, f ) consists of a set V of vertices, a set E of edges, and a function f:EVV.
E.g., V=web pages,E=hyperlinks. The WWW isa directed multigraph...
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Types of Graphs: Summary
Summary of the book’s definitions. Keep in mind this terminology is not fully
standardized across different authors...
TermEdgetype
Multipleedges ok?
Self-loops ok?
Simple graph Undir. No NoMultigraph Undir. Yes NoPseudograph Undir. Yes YesDirected graph Directed No YesDirected multigraph Directed Yes Yes
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§8.2: Graph Terminology
You need to learn the following terms: Adjacent, connects, endpoints, degree,
initial, terminal, in-degree, out-degree, complete, cycles, wheels, n-cubes, bipartite, subgraph, union.
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Adjacency
Let G be an undirected graph with edge set E. Let eE be (or map to) the pair {u,v}. Then we say:
u, v are adjacent / neighbors / connected.Edge e is incident with vertices u and v.Edge e connects u and v.Vertices u and v are endpoints of edge e.
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Degree of a Vertex
Let G be an undirected graph, vV a vertex. The degree of v, deg(v), is its number of
incident edges. (Except that any self-loops are counted twice.)
A vertex with degree 0 is called isolated. A vertex of degree 1 is called pendant.
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Handshaking Theorem
Let G be an undirected (simple, multi-, or pseudo-) graph with vertex set V and edge set E. Then
Corollary: Any undirected graph has an even number of vertices of odd degree.
EvVv
2)deg(
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Directed Adjacency
Let G be a directed (possibly multi-) graph, and let e be an edge of G that is (or maps to) (u,v). Then we say:– u is adjacent to v, v is adjacent from u– e comes from u, e goes to v.– e connects u to v, e goes from u to v– the initial vertex of e is u– the terminal vertex of e is v
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Directed Degree
Let G be a directed graph, v a vertex of G.– The in-degree of v, deg(v), is the number of
edges going to v.– The out-degree of v, deg(v), is the number of
edges coming from v.– The degree of v, deg(v):deg(v)+deg(v), is the
sum of v’s in-degree and out-degree.
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Directed Handshaking Theorem
Let G be a directed (possibly multi-) graph with vertex set V and edge set E. Then:
Note that the degree of a node is unchanged by whether we consider its edges to be directed or undirected.
EvvvVvVvVv
)deg(2
1)(deg)(deg
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Special Graph Structures
Special cases of undirected graph structures: Complete graphs Kn
Cycles Cn
Wheels Wn
n-Cubes Qn
Bipartite graphs Complete bipartite graphs Km,n
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Complete Graphs
For any nN, a complete graph on n vertices, Kn, is a simple graph with n nodes in which every node is adjacent to every other node: u,vV: uv{u,v}E.
K1 K2K3
K4 K5 K6
Note that Kn has edges.2
)1(1
1
nni
n
i
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Cycles
For any n3, a cycle on n vertices, Cn, is a simple graph where V={v1,v2,… ,vn} and E={{v1,v2},{v2,v3},…,{vn1,vn},{vn,v1}}.
C3 C4 C5 C6 C7C8
How many edges are there in Cn?
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Wheels
For any n3, a wheel Wn, is a simple graph obtained by taking the cycle Cn and adding one extra vertex vhub and n extra edges {{vhub,v1}, {vhub,v2},…,{vhub,vn}}.
W3 W4 W5 W6 W7W8
How many edges are there in Wn?
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n-cubes (hypercubes)
For any nN, the hypercube Qn is a simple graph consisting of two copies of Qn-1 connected together at corresponding nodes. Q0 has 1 node.
Q0Q1 Q2 Q3
Q4
Number of vertices: 2n. Number of edges:Exercise to try!
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Def’n.: A graph G=(V,E) is bipartite (two-part) iff V = V1∩V2 where V1V2= and eE: v1V1,v2V2: e={v1,v2}.
In English: The graph canbe divided into two partsin such a way that all edges go between the two parts.
Bipartite Graphs
V1 V2This definition can easily be adapted for the case of multigraphs and directed graphs as well. Can represent with
zero-one matrices.
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Complete Bipartite Graphs
For m,nN, the complete bipartite graph Km,n is a bipartite graph where |V1| = m, |V2| = n, and E = {{v1,v2}|v1V1 v2V2}.– That is, there are m nodes
in the left part, n nodes in the right part, and every node in the left part is connected to every node in the right part.
K4,3
Km,n has _____ nodesand _____ edges.
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Subgraphs
A subgraph of a graph G=(V,E) is a graph H=(W,F) where WV and FE.
G H
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Graph Unions
The union G1G2 of two simple graphs G1=(V1, E1) and G2=(V2,E2) is the simple graph (V1V2, E1E2).
a b c
d e
a b c
d f
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§8.3: Graph Representations & Isomorphism
Graph representations:– Adjacency lists.– Adjacency matrices.– Incidence matrices.
Graph isomorphism:– Two graphs are isomorphic iff they are identical
except for their node names.
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Adjacency Lists
A table with 1 row per vertex, listing its adjacent vertices.a b
dc
fe
VertexAdjacentVertices
ab
b, ca, c, e, f
c a, b, fde bf c, b
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Directed Adjacency Lists
1 row per node, listing the terminal nodes of each edge incident from that node.
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Adjacency Matrices
A way to represent simple graphs– possibly with self-loops.
Matrix A=[aij], where aij is 1 if {vi, vj} is an edge of G, and is 0 otherwise.
Can extend to pseudographs by letting each matrix elements be the number of links (possibly >1) between the nodes.
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Graph Isomorphism
Formal definition:– Simple graphs G1=(V1, E1) and G2=(V2, E2) are
isomorphic iff a bijection f:V1V2 such that a,bV1, a and b are adjacent in G1 iff f(a) and f(b) are adjacent in G2.
– f is the “renaming” function between the two node sets that makes the two graphs identical.
– This definition can easily be extended to other types of graphs.
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Graph Invariants under Isomorphism
Necessary but not sufficient conditions for G1=(V1, E1) to be isomorphic to G2=(V2, E2):
– We must have that |V1|=|V2|, and |E1|=|E2|.– The number of vertices with degree n is the same
in both graphs.– For every proper subgraph g of one graph, there
is a proper subgraph of the other graph that is isomorphic to g.
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Isomorphism Example
If isomorphic, label the 2nd graph to show the isomorphism, else identify difference.
a
b
cd
ef
b
d
a
efc
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Are These Isomorphic?
If isomorphic, label the 2nd graph to show the isomorphism, else identify difference.
ab
c
d
e
• Same # of vertices
• Same # of edges
• Different # of verts of degree 2! (1 vs 3)
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§8.4: Connectivity
In an undirected graph, a path of length n from u to v is a sequence of adjacent edges going from vertex u to vertex v.
A path is a circuit if u=v. A path traverses the vertices along it. A path is simple if it contains no edge more
than once.
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Paths in Directed Graphs
Same as in undirected graphs, but the path must go in the direction of the arrows.
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Connectedness
An undirected graph is connected iff there is a path between every pair of distinct vertices in the graph.
Theorem: There is a simple path between any pair of vertices in a connected undirected graph.
Connected component: connected subgraph A cut vertex or cut edge separates 1
connected component into 2 if removed.
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Directed Connectedness
A directed graph is strongly connected iff there is a directed path from a to b for any two vertices a and b.
It is weakly connected iff the underlying undirected graph (i.e., with edge directions removed) is connected.
Note strongly implies weakly but not vice-versa.
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Paths & Isomorphism
Note that connectedness, and the existence of a circuit or simple circuit of length k are graph invariants with respect to isomorphism.
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Counting Paths w Adjacency Matrices
Let A be the adjacency matrix of graph G. The number of paths of length k from vi to vj
is equal to (Ak)i,j. – The notation (M)i,j denotes mi,j where
[mi,j] = M.
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§8.5: Euler & Hamilton Paths
An Euler circuit in a graph G is a simple circuit containing every edge of G.
An Euler path in G is a simple path containing every edge of G.
A Hamilton circuit is a circuit that traverses each vertex in G exactly once.
A Hamilton path is a path that traverses each vertex in G exactly once.
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Bridges of Königsberg Problem
Can we walk through town, crossing each bridge exactly once, and return to start?
A
B
C
D
The original problem Equivalent multigraph
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Euler Path Theorems
Theorem: A connected multigraph has an Euler circuit iff each vertex has even degree.
Theorem: A connected multigraph has an Euler path (but not an Euler circuit) iff it has exactly 2 vertices of odd degree.– One is the start, the other is the end.
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Euler Circuit Algorithm
Begin with any arbitrary node. Construct a simple path from it till you get
back to start. Repeat for each remaining subgraph, splicing
results back into original cycle.
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Round-the-World Puzzle
Can we traverse all the vertices of a dodecahedron, visiting each once?`
Dodecahedron puzzle Equivalentgraph
Pegboard version
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Hamiltonian Path Theorems
Dirac’s theorem: If (but not only if) G is connected, simple, has n3 vertices, and v deg(v)n/2, then G has a Hamilton circuit.– Ore’s corollary: If G is connected, simple, has
n≥3 nodes, and deg(u)+deg(v)≥n for every pair u,v of non-adjacent nodes, then G has a Hamilton circuit.